Truss community search in uncertain graphs

Given an uncertain graph, community search is used to return dense subgraphs that contain the query vertex and satisfy the probability constraint. With the proliferation of uncertain graphs in practical applications, community search has become increasingly important in practical applications to help users make decisions in advertising recommendations, conference organization, etc. However, existing approaches for community search still suffer from two problems. First, they may return subgraphs that cannot meet users’ expectations on structural cohesiveness, due to the existence of cut-vertices/edges. Second, they use floating-point division to update the probability of each edge during computation, resulting in inaccurate results. In this paper, we study community search on uncertain graphs and propose efficient algorithms to address the above two problems. We first propose a novel community model, namely triangle-connected \((k,\gamma )\) $(k,\gamma )$ -truss community, to return communities with enhanced cohesiveness. Then, we propose an online algorithm that uses a batch-recalculation strategy to guarantee the accuracy. To improve the performance of community search, we propose an index-based approach. This index organizes all the triangle-connected \((k,\gamma )\) $(k,\gamma )$ -truss communities using a forest structure and maintains the mapping relationship from vertices in the uncertain graph to communities in the index. Based on this index, we can get the results of community search easily, without the costly operation as the online approach does. Finally, we conduct rich experiments on 10 real-world graphs. The experimental results verified the effectiveness and efficiency of our approaches.